Functional Classification and Pathway Identification
Principal component analysis (PCA) was performed to assess variability
for the soybean genotypes under control and drought conditions using the
PRCOMP command with default setting in the R software package (Robinsonet al. , 2010). Log2-transformed FPKM values of
the DEGs were used for K-means clustering using Pearson correlation in
Microarray Experiment Viewer (MeV, v4.9) software. AgriGO v2.0 database
(http://systemsbiology.cau.edu.cn/agriGOv2/) was implemented for
GO enrichment analysis of DEGs by using Glycine max as reference
background, and DEGs were classified into three major categories:
biological processes (BP), cellular components (CC) and molecular
function (MF) (Tian et al. , 2017). Reduce and Visualize GO
analyses /REVIGO (http://revigo.irb.hr/) were performed to remove
the redundancy of GO terms using SimRel semantic similarity measure,
with an allowed similarity of 0.7 (medium), and the results were
displayed as scatter plot (Supek et al. , 2011). Up- and
down-regulated transcripts were subjected to MapMan software version
3.6.0 RC1 (http://mapman.gabipd.org/web/guest/mapman) (Usadel et
al. , 2009). Mapped gene intensity of fold change of various pathways
(both biological or metabolic) were plotted by blue and red schema. The
KEGG (Kyoto encyclopedia of gene and genome) was further utilized for
pathway enrichment analysis of DEGs (Kanehisa et al. , 2008).